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Twin Contrastive Learning with Noisy Labels

Twin Contrastive Learning with Noisy Labels

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhizhong Huang Junping Zhang Hongming Shan Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science Fudan University Shanghai China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai China

Learning from noisy data is a challenging task that sig-nificantly degenerates the model performance. In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification. Specifically, we construct a Gaussian mixture model (GMM) over the representations by injecting the supervised model predictions into GMM to link label- free latent variables in GMM with label-noisy annotations. Then, TCL detects the examples with wrong labels as the out- of-distribution examples by another two-component GMM, taking into account the data distribution. We further propose a cross-supervision with an entropy regularization loss that bootstraps the true targets from model predictions to handle the noisy labels. As a result, TCL can learn discriminative representations aligned with estimated labels through mixup and contrastive learning. Extensive experimental results on several standard benchmarks and real-world datasets demonstrate the superior performance of TCL. In particular, TCL achieves 7.5% improvements on CIFAR-10 with 90% noisy label-an extremely noisy scenario. The source code is available at https://***/Hzzone/TCL.

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Uncovering the Missing pattern: Unified Framework Towards Trajectory Imputation and Prediction

Uncovering the Missing Pattern: Unified Framework Towards Tr...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yi Xu Armin Bazarjani Hyung-gun Chi Chiho Choi Yun Fu Northeastern University Honda Research Institute USA University of Southern California Purdue University Samsung Semiconductor US

Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. This limitation inevitably hinders the accuracy of trajectory prediction. To address this issue, our paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously. Specifically, we introduce a novel Multi-Space Graph Neural Network (MS-GNN) that can extract spatial features from incomplete observations and leverage missing patterns. Additionally, we employ a Conditional VRNN with a specifically designed Temporal Decay (TD) module to capture temporal dependencies and temporal missing patterns in incomplete trajectories. The inclusion of the TD module allows for valuable information to be conveyed through the temporal flow. We also curate and benchmark three practical datasets for the joint problem of trajectory imputation and prediction. Extensive experiments verify the exceptional performance of our proposed method. As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.

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DynamicDet: A Unified Dynamic Architecture for Object Detection

DynamicDet: A Unified Dynamic Architecture for Object Detect...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhihao Lin Yongtao Wang Jinhe Zhang Xiaojie Chu Wangxuan Institute of Computer Technology Peking University

Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we propose a dynamic framework for object detection, named DynamicDet. Firstly, we carefully design a dynamic architecture based on the nature of the object detection task. Then, we propose an adaptive router to analyze the multi-scale information and to decide the inference route automatically. We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors. Last, we present a variable-speed inference strategy, which helps to realize a wide range of accuracy-speed trade-offs with only one dynamic detector. Extensive experiments conducted on the COCO benchmark demonstrate that the proposed DynamicDet achieves new state-of-the-art accuracy-speed trade-offs. For instance, with comparable accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is available at https://***/VDIGPKU/DynamicDet.

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Trade-off between Robustness and Accuracy of vision Transformers

Trade-off between Robustness and Accuracy of Vision Transfor...

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conference on computer vision and pattern recognition (CVPR)

作者： Yanxi Li Chang Xu Faculty of Engineering School of Computer Science The University of Sydney Australia

Although deep neural networks (DNNs) have shown great successes in computer vision tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the natural accuracy and robustness to such perturbations, which is mainly caused by the existence of robust non-predictive features and non-robust predictive features. Recent empirical analyses find vision Transformers (ViTs) are inherently robust to various kinds of perturbations, but the aforementioned trade-off still exists for them. In this work, we propose Trade-off between Robustness and Accuracy of vision Transformers (TORA-ViTs), which aims to efficiently transfer ViT models pretrained on natural tasks for both accuracy and robustness. TORA-ViTs consist of two major components, including a pair of accuracy and robustness adapters to extract predictive and robust features, respectively, and a gated fusion module to adjust the trade-off. The gated fusion module takes outputs of a pre-trained ViT block as queries and outputs of our adapters as keys and values, and tokens from different adapters at different spatial locations are compared with each other to generate attention scores for a balanced mixing of predictive and robust features. Experiments on ImageNet with various robust benchmarks show that our TORA-ViTs can efficiently improve the robustness of naturally pretrained ViTs while maintaining competitive natural accuracy. Our most balanced setting (TORA-ViTs with λ= 0.5) can maintain 83.7% accuracy on clean ImageNet and reach 54.7% and 38.0% accuracy under FGSM and PGD white-box attacks, respectively. In terms of various ImageNet variants, it can reach 39.2% and 56.3% accuracy on ImageNet-A and ImageNet-R and reach 34.4% mCE on ImageNet-C.

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Spatial-Frequency Mutual Learning for Face Super-Resolution

Spatial-Frequency Mutual Learning for Face Super-Resolution

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conference on computer vision and pattern recognition (CVPR)

作者： Chenyang Wang Junjun Jiang Zhiwei Zhong Xianming Liu School of Computer Science and Technology Harbin Institute of Technology Harbin China

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved significant breakthroughs. However, existing FSR methods either have a fixed receptive field or fail to maintain facial structure, limiting the FSRperformance. To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field. Relying on the Fourier transform, we devise a spatial-frequency mutual network (SFMNet) for FSR, which is the first FSR method to explore the correlations between spatial and frequency domains as far as we know. To be specific, our SFMNet is a two-branch network equipped with a spatial branch and a frequency branch. Benefiting from the property of Fourier transform, the frequency branch can achieve image-size receptive field and capture global dependency while the spatial branch can extract local dependency. Considering that these dependencies are complementary and both favorable for FSR, we further develop a frequency-spatial interaction block (FSIB) which mutually amalgamates the complementary spatial and frequency information to enhance the capability of the model. Quantitative and qualitative experimental results show that the proposed method out-performs state-of-the-art FSR methods in recovering face images. The implementation and model will be released at https://***/wcy-cs/SFMNet.

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PATS: Patch Area Transportation with Subdivision for Local Feature Matching

PATS: Patch Area Transportation with Subdivision for Local F...

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conference on computer vision and pattern recognition (CVPR)

作者： Junjie Ni Yijin Li Zhaoyang Huang Hongsheng Li Hujun Bao Zhaopeng Cui Guofeng Zhang State Key Lab of CAD&CG Zhejiang University ZJU-SenseTime Joint Lab of 3D Vision Multimedia Laboratory The Chinese University of Hong Kong

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at https://***/pats/.

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chuan Tang Xi Yang Bojian Wu Zhizhong Han Yi Chang School of Artificial Intelligence Jilin University China MoE Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China Zhejiang University The Department of Computer Science Wayne State University USA

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

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On the Importance of Accurate Geometry Data for Dense 3D vision Tasks

On the Importance of Accurate Geometry Data for Dense 3D Vis...

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conference on computer vision and pattern recognition (CVPR)

作者： HyunJun Jung Patrick Ruhkamp Guangyao Zhai Nikolas Brasch Yitong Li Yannick Verdie Jifei Song Yiren Zhou Anil Armagan Slobodan Ilic Ales Leonardis Nassir Navab Benjamin Busam Technical University of Munich 3Dwe.ai Huawei Noah's Ark Lab Siemens AG

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problematic for structure from motion and stereo, reflective material poses issues for active sensing, and distances for translucent objects are intricate to measure with existing hardware. Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. These effects remain unnoticed if the sensor measurement is considered as ground truth during the evaluation. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction. We rigorously show the significant impact of sensor characteristics on the learned predictions and notice generalisation issues arising from various technologies in everyday household environments. For evaluation, we introduce a carefully designed dataset 1 1 dataset available at https://***/Junggy/HAMMER-dataset comprising measurements from commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular RGB+P. Our study quantifies the considerable sensor noise impact and paves the way to improved dense vision estimates and targeted data fusion.

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Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency is All You Need

Towards Realistic Long-Tailed Semi-Supervised Learning: Cons...

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conference on computer vision and pattern recognition (CVPR)

作者： Tong Wei Kai Gan School of Computer Science and Engineering Southeast University Nanjing China

While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in many real-world classification problems, existing LTSSL algorithms typically assume that the class distributions of labeled and unlabeled data are almost identical. Those LTSSL algorithms built upon the assumption can severely suffer when the class distributions of labeled and unlabeled data are mismatched since they utilize biased pseudo-labels from the model. To alleviate this issue, we propose a new simple method that can effectively utilize unlabeled data of unknown class distributions by introducing the adaptive consistency regularizer (ACR). ACR realizes the dynamic refinery of pseudolabels for various distributions in a unified formula by estimating the true class distribution of unlabeled data. Despite its simplicity, we show that ACR achieves state-of-the-art performance on a variety of standard LTSSL benchmarks, e.g., an averaged 10% absolute increase of test accuracy against existing algorithms when the class distributions of labeled and unlabeled data are mismatched. Even when the class distributions are identical, ACR consistently outper-forms many sophisticated LTSSL algorithms. We carry out extensive ablation studies to tease apart the factors that are most important to ACR's success. Source code is available at https://***/Gank0078/ACR.

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Masked Jigsaw Puzzle: A Versatile Position Embedding for vision Transformers

Masked Jigsaw Puzzle: A Versatile Position Embedding for Vis...

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conference on computer vision and pattern recognition (CVPR)

作者： Bin Ren Yahui Liu Yue Song Wei Bi Rita Cucchiara Nicu Sebe Wei Wang University of Pisa Italy University of Trento Italy Tencent AI Lab China University of Modena and Reggio Emilia Italy Beijing Jiaotong University China

Position Embeddings (PEs), an arguably indispensable component in vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack; 2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy; 3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://***/yhlleo/MJP.

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